Waste to Catalyst: Synthesis of Catalysts from Sewage Sludge of the Mining, Steel, and Petroleum Industries

sustainability

Article Waste to Catalyst: Synthesis of Catalysts from Sewage Sludge of the Mining, Steel, and Petroleum Industries

Gabriela Castro-León, Erik Baquero-Quinteros, Bryan G. Loor, Jhoselin Alvear, Diego E. Montesdeoca Espín, Andrés De La Rosa and Carolina Montero-Calderón *

Chemical Engineering Faculty, Universidad Central del Ecuador, Ritter s/n & Bolivia, Quito 17-01-3972, Ecuador; [email protected] (G.C.-L.); [email protected] (E.B.-Q.); [email protected] (B.G.L.); [email protected] (J.A.); [email protected] (D.E.M.E.); [email protected] (A.D.L.R.) * Correspondence: [email protected]

Received: 5 August 2020; Accepted: 22 September 2020; Published: 25 November 2020

Abstract: The generation of sewage sludge presents a problem for several manufacturing companies as it results from industrial processes or effluent treatment systems. The treatment of this type of waste requires high economic investment, for this reason, it is necessary to find alternatives to recover the valuable materials of the sludges. In this study, metal catalysts were synthesized using waste sludge from the steel, mining, and hydrocarbon industries. The waste sludge was subjected to thermal treatments for the removal of organic content and the reduction of metals with hydrogen current to activate their catalytic properties. The sludge and synthesized catalysts were analyzed to determine their physical, chemical, thermoenergetic, and catalytic properties. Catalytic activity was evaluated using CO chemisorption and by thermal–catalytic decomposition of crude oil. The best conditions for synthesizing the catalysts were a calcination temperature between 300 and 500 ◦C and a reduction temperature between 300 and 900 ◦C. The catalysts presented a specific surface between 2.33 and 16.78 m2/g. The catalytic material had a heat capacity between 0.7 and 1.2 kJ/kg K. The synthesized · materials presented catalytic activity comparable to that of commercial catalysts. With this recovery technique, the industrial waste can be valorized, obtaining catalyst derived from the sludges and promoting the circular economy of manufacturing companies.

Keywords: sewage sludge; mining tailings; steelworks; oil tank bottoms; catalysts; waste valorization; circular economy

1. Introduction Global waste generation is expected to double by 2025, of which industrial waste will comprise nearly 50% of the total. Global waste generation is projected to reach 11 million tons/day by 2100 [1]. Production processes use raw materials based on metals or containing metals, and some wastes are generated as sludge in the production stages, which often cannot be minimized. These process sludges, as well as those generated in water treatment systems, are considered hazardous waste according to Ecuadorian regulations [2]; therefore, sludge must be technically disposed of through environmental management. In Ecuador in 2017, it was estimated that 300 thousand tons of sewage sludge was generated and that companies invested approximately $200 million in environmental prevention processes [3]. Thus, Ecuador seeks to promote sustainable production and responsible consumption alternatives that include the recovery of raw materials from waste as part of its new economic development agenda based on the circular economy [4]. The economic potential of organic sewage sludge valorization for biogas production, co-incineration, or as a fertilizer in landscaping and agriculture is considered to be a possible

Sustainability 2020, 12, 9849; doi:10.3390/su12239849 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 9849 2 of 16 line of action for the support of the social and solidarity economy of communities in developing countries [5]. Alternatives involving the encapsulation of sludge as a part of construction materials have been proposed, such as the incorporation of tailings from lead mine washing plants in ceramics for bricks [6], the use of gold mine tailings as cement mortar [7,8], and the preparation of high-porosity bricks by utilizing red mud and mine tailings [9]. Some recovery alternatives for the synthesis of chemicals for the retention of metal contaminants have been reported, such as the use of residuals from drinking water treatment as an adsorbent for the retention of phosphorus compounds [10], for metal removal from electroplating wastewater eﬄuent [11], or the adsorption of heavy metal ions by porous material prepared with silicate tailings [12]. The recovery of metal components present in sludge includes the production of iron-based secondary raw material from bauxite tailings and red muds [13] and gold recovery from mining waste by thiosulphate leaching [14]. Sludges from the electroplating process have been used to produce inorganic pigments for applications in ceramic glazes [15], and kimberlite tailings have been used in carbon capture [16]. The synthesis of metal catalysts from sewage sludge and their application in the oxidation of hydrocarbons has been reported since 2000 [17]. Since then, similar applications have been reported for multiple purposes, such as the oxidative dehydrogenation of propane with textile sludge-based catalysts [18], for the removal of volatile organic compounds using ferric catalysts derived from sludge produced by water puriﬁcation [19], and for the N H insertion reaction using catalysts derived from − sewage sludge of carbonaceous materials [20]. Our research group has studied catalytic activity in crude cracking reactions using a catalyst derived from sludge resulting from wastewater treatment by the textile [21], the galvanoplasty [22], and tannery [23] industries, obtaining yields comparable to those for commercial catalysts. These catalysts have also been tested in terms of the catalytic decomposition of methane [24], in which conversion rates of reactants higher than 90% were obtained. Due to this, materials that have part of the organic or polymeric matrix with oxides or metals [25,26] can be used as photocatalyst. This work evaluated the possibility of synthesizing catalysts from waste sludge from three industries with the potential for economic development in Ecuador: sludge from the wastewater treatment plants of steelworks, metal mining tailings, and tank bottom sludge from hydrocarbon storage. These sludges contain metals such as iron, gold, and copper that could confer catalytic properties. After synthesis, the physical, chemical, and thermoenergetic properties were characterized, and the catalytic capacity of the materials was determined. The catalysts derived from mining tailings presented good thermal stability and a catalytic activity comparable to commercial catalysts. This alternative method to valorize hazardous sludges could aid in the implementation of the national development plans based on the circular economy.

2. Results

2.1. Samples Identification The sludge samples were supplied by three different manufacturing companies located in Ecuador (Table1). Company 1, located in the south of Ecuador, is a mine tailing deposit where artisanal mining waste is collected. This company generates two types of sludge: aged tailings (S1) and fresh tailings (S2). Company 2, located in the center of Ecuador, is a steelwork facility where the residual sludge comes from its wastewater treatment plant (S3). Company 3 is located in the Amazon region of Ecuador, and the sludge (S4) is bottom waste from petroleum storage tanks. The catalytic materials derived from each of these sludges were identified as C1, C2, C3, and C4 (Figure1a–d). Sustainability 2020, 12, 9849 3 of 16

Sustainability 2020, 12, x FOR PEER REVIEW 3 of 16 Table 1. Nomenclature used for the diﬀerent studied sludges. Table 1. Nomenclature used for the different studied sludges. Company Type of Sludge Sludge Catalytic Material 1aCompany Aged Type mining of Sludge tailings Sludge S1 Catalytic Material C1 1b1a Fresh Aged mining mining tailingstailings S1 S2 C1 C2 21b Wastewater Fresh sludge mining from tailings a steelwork S2 S3 C2 C3 32 Bottom Wastewater sludge from sludge petroleum from a steelwork storage tanks S3 S4 C3 C4 3 Bottom sludge from petroleum storage tanks S4 C4

(a) (b)

FigureFigure 1. Dried1. Dried fresh fresh mine mine tailings tailings S1 ( aS1), aged(a), aged mine mine tailings tailings S2 (b), S2 wastewater (b), wastewater sludge sludge from a steelworkfrom a S3steelwork (c), and bottom S3 (c), sludgeand bottom from sludge petroleum from storagepetroleum tanks storage S4 (d tanks). S4 (d).

2.2.2.2. Catalyst Catalyst Preparation Preparation TheThe preparation preparation of of catalysts catalysts has has beenbeen describeddescribed in previous previous works works by by our our research research group group [21,22]. [21,22 ]. TheThe samples samples were were gradually gradually dried dried in in order order toto preventprevent damage to to their their porous porous structure. structure. At At the the same same time,time, temperatures temperatures and and calcination calcination timestimes werewere chosen based based on on the the metals metals present present in inthe the sludge, sludge, namelynamely Au Au [27 [27–29],–29], Fe Fe [30 [30–33],–33], and and Cu Cu [[29,34,35].29,34,35]. The range range of of calcination calcination te temperaturesmperatures was was selected selected accordingaccording to to the the possible possible metals metals containedcontained inin the samples to to avoid avoid metal metal sintering sintering due due to tohigh high temperatures.temperatures. The The conditions conditions for for sludge sludge dryingdrying andand calcination are are given given in in Table Table 2.2 .

TableTable 2. 2.Preparation Preparation conditionsconditions of the the catalysts. catalysts. Catalytic Drying Drying Calcination Calcination Company Catalytic Drying Drying Calcination Calcination Company Material Temperature, °C Time, h Temperature, °C Time, h Material Temperature, ◦C Time, h Temperature, ◦C Time, h 1a C1 110 4.5 300 4 1a C1 110 4.5 300 4 1b 1bC2 C2 120 120 4.5 4.5 300 300 4 4 2 2C3 C3 120 120 4.5 4.5 500 500 4 4 3 3C4 C4 130 130 4.5 4.5 450 450 2 2

Calcination was performed at three temperatures based on the possible metals present in the Calcination was performed at three temperatures based on the possible metals present in the sludge, which were above 300 °C to ensure the removal of organic material while being below 600 sludge, which were above 300 C to ensure the removal of organic material while being below 600 C. °C. Additionally, a heating ramp◦ was used to prevent breaking of the porous structure caused by◦ Additionally,abrupt changes a heating in temperature ramp was [36] used and to to prevent avoid breakingthe structural of the reordering porous structure of solid particles, caused by which abrupt Sustainability 2020, 12, 9849 4 of 16

Sustainability 2020, 12, x FOR PEER REVIEW 4 of 16 changes in temperature [36] and to avoid the structural reordering of solid particles, which causes a decreasecauses a indecrease the number in the ofnumber active of sites. active The sites. synthesized The synthesized catalysts catalysts are depicted are depicted in Figure in 2Figurea–d. 2a– d.

(a) (b)

FigureFigure 2. Catalyst2. Catalyst derived derived from from fresh fresh mine mine tailings tailings C1 (aC1), aged(a), aged mine mine tailings tailings C2 (b C2), wastewater (b), wastewater sludge fromsludge a steel from industry a steel C3industry (c), and C3 bottom (c), and sludge bottom from sludge petroleum from petroleum storage storage tanks C4 tanks (d). C4 (d).

2.3.2.3. Characterization Characterization of of Chemical Chemical Properties Properties of of Sludge Sludge andand CatalystsCatalysts In orderIn order to to verify verify the the elimination elimination of of elemental elemental componentscomponents contained contained in in the the sludge sludge after after drying drying andand calcination, calcination, the the percentages percentages of of carbon, carbon, hydrogen,hydrogen, nitrogen, and and sulfur sulfur (CHNS (CHNS analysis) analysis) were were measuredmeasured in thein the sludge sludge (S) (S) and and catalysts catalysts (C). (C). TheseThese results,results, as shown shown in in Table Table 3,3, illustrate illustrate low low nitrogen nitrogen contentscontents in thein the samples samples from from the the analyzed analyzed industries. industries. The sludge from from oil oil storage storage tanks tanks had had residual residual metals that were impregnated with oil and other hydrocarbons [37]; therefore, the catalysts metals that were impregnated with oil and other hydrocarbons [37]; therefore, the catalysts synthesized synthesized from these materials had the highest contents of carbon, hydrogen, and sulfur when from these materials had the highest contents of carbon, hydrogen, and sulfur when compared to the compared to the other catalysts. The difference in the storage time of mine tailings sludge did not other catalysts. The diﬀerence in the storage time of mine tailings sludge did not have a signiﬁcant have a significant impact on the elemental composition; in the case of sludge from wastewater impact on the elemental composition; in the case of sludge from wastewater treatment in the steel treatment in the steel industry, its carbon content is attributable to the steel formulation itself. industry, its carbon content is attributable to the steel formulation itself. Table 3. Elemental composition of sludge (S) and catalysts (C). Table 3. Elemental composition of sludge (S) and catalysts (C). Sample Nitrogen, wt% Carbon, wt% Hydrogen, wt% Sulfur, wt% SampleS1 Nitrogen,0.0 wt% Carbon, 0.5 wt% Hydrogen, 0.1 wt% 3.6 Sulfur, wt% S1C1 0.00.0 0.5 0.3 0.1 0.1 2.0 3.6 C1S2 0.00.0 0.3 0.2 0.1 0.1 3.4 2.0 S2C2 0.00.0 0.2 0.2 0.1 0.1 2.9 3.4 C2S3 0.00.0 0.2 1.0 0.1 0.1 1.0 2.9 S3C3 0.00.0 1.0 0.1 0.1 0.1 0.9 1.0 C3S4 0.00.4 35.90.1 5.0 0.1 7.4 0.9 S4C4 0.40.0 35.9 5.2 0.1 5.0 4.4 7.4 AccuracyC4 = ±0.2%, CHNS 0.0 analysis considering 5.2 that balance mass 0.1 is closed with oxygen 4.4 content. Accuracy = 0.2%, CHNS analysis considering that balance mass is closed with oxygen content. Despite the different± nature of the sludges, it was observed that all tend to exhibit a decreased elemental composition after calcination. The elimination of elemental compounds prevents the formation of compounds that can deactivate the catalysts, such as sulfur oxide [38]. Sustainability 2020, 12, 9849 5 of 16

Despite the diﬀerent nature of the sludges, it was observed that all tend to exhibit a decreased elemental composition after calcination. The elimination of elemental compounds prevents the formation of compounds that can deactivate the catalysts, such as sulfur oxide [38]. SustainabilityThe metallic 2020, 12 contents, x FOR PEER of REVIEW the sludge and catalysts were measured using atomic absorption (AA)5 of 16 tests. Table4 shows that the four synthesized catalysts are predominantly iron in content, which is characteristicThe metallic of industrial contents waste of the [39 sludge]. The and residue cataly andsts catalyst were measured from the steelusing industry atomic haveabsorption the highest (AA) irontests. content Table and4 shows traces that of chromiumthe four synthesized and copper, cata whichlysts areare probablypredominantly attributable iron in to content, the compounds which is usedcharacteristic during the of water industrial treatment waste from [39]. which The thisresidu sludgee and originates. catalyst from the steel industry have the highestTraces iron of content gold and and copper, traces typical of chromium of mining and activity, copper, could which be are identiﬁed probably in theattributable tailing sludge. to the Coppercompounds traces used were during detected the in water the sludge treatmen fromt from the bottoms which this of hydrocarbon sludge originates. storage tanks. The content of Fe inTraces the catalystof gold isand around copper, 2.5–7.5%, typical similarof mining to theactivity, iron contentcould be in identified the catalyst in usedthe tailing by selective sludge. catalyticCopper reductiontraces were of NOxdetected [40]. in the sludge from the bottoms of hydrocarbon storage tanks. The content of Fe in the catalyst is around 2.5–7.5%, similar to the iron content in the catalyst used by selective catalyticTable reduction 4. Metallic of content NOx for[40]. sludge and catalysts measure by atomic absorption.

Table 4. MetallicMetal content Sample for sludge wt% and catalysts Sample measure by wt%atomic absorption. Fe S1 3.7 0.5 C1 3.2 0.5 Metal Sample ± wt% Sample wt% ± Fe S2 3.5 0.5 C2 3.8 0.5 Fe S1 3.7± ± 0.5 C1 3.2 ± 0.5± FeFe S3 S2 7.6 3.5 ±0.5 0.5 C3C2 3.8 7.5± 0.5 0.5 ± ± FeFe S4 S3 2.6 7.6 ±0.5 0.5 C4C3 7.5 2.5± 0.5 0.5 Fe S4 2.6± ± 0.5 C4 2.5 ± 0.5±

2.4. Physical and Physisorption Properties of Catalysts 2.4. Physical and Physisorption Properties of Catalysts The grain size distribution of the catalysts is relevant for evaluating the effects of internal diffusion The grain size distribution of the catalysts is relevant for evaluating the effects of internal and determining the minimum fluidization velocity and pressure drop for applications of these catalysts diffusion and determining the minimum fluidization velocity and pressure drop for applications of in fixed and fluidized beds. these catalysts in fixed and fluidized beds. According to the obtained results (Figure3), the grain size distribution of catalysts is mostly in the According to the obtained results (Figure 3), the grain size distribution of catalysts is mostly in range of 160–320 µm, with C1 grain being the largest (240–320 µm). Usually, in cracking applications, the range of 160–320 μm, with C1 grain being the largest (240–320 μm). Usually, in cracking the mean grain size of the catalyst is between 70 and 110 µm or 20 and 90 µm [41]. applications, the mean grain size of the catalyst is between 70 and 110 μm or 20 and 90 μm [41].

Figure 3. Grain size distribution of catalysts C1–C4 determined by granulometry. Figure 3. Grain size distribution of catalysts C1–C4 determined by granulometry.

TheThe studystudy ofof thethe speciﬁcspecific surfacesurface areaarea allows allows thethe physisorption physisorption capacitycapacity ofof thethe catalystscatalysts toto bebe determined,determined, whichwhich isis anan essentialessential propertyproperty in in catalytic catalytic reactions. reactions. TheThe measurementmeasurement waswas mademade byby applyingapplying the the single-point single-point BET BET method, method, and and the the results results are are presented presented in in Table Table5. 5.

Table 5. Specific surface of synthetized catalysts.

Catalyst Specific Surface, m2 g−1 C1 4.25 ± 0.04 C2 3.03 ± 0.04 C3 7.97 ± 0.04 C4 16.78 ± 0.04

When the sludges were calcined, their specific surface areas decreased. This process eliminates surface defects, making the solid structure derived from sludge more uniform and compact, causing Sustainability 2020, 12, 9849 6 of 16

Table 5. Speciﬁc surface of synthetized catalysts.

2 1 Catalyst Speciﬁc Surface, m g− C1 4.25 0.04 ± C2 3.03 0.04 ± C3 7.97 0.04 ± C4 16.78 0.04 ±

When the sludges were calcined, their specific surface areas decreased. This process eliminates surface defects, making the solid structure derived from sludge more uniform and compact, causing the size ofSustainability some pores 2020, 12 to, x decrease,FOR PEER REVIEW which implies that a smaller amount of gas is adsorbed during6 of 16 the physisorptionthe size of some with pores nitrogen. to decrease, The catalysts which implies synthesized that a smaller in this amount study of present gas is adsorbed lower specific during the surface areasphysisorption than those derived with nitrogen. from textile The catalysts wastewater synthesi treatmentzed in this [22 study] or sludgepresent andlower ferric specific sludge surface derived fromareas water than purification those derived [19]. from textile wastewater treatment [22] or sludge and ferric sludge derived from water purification [19]. 2.5. Temperature-Programmed Reduction (TPR) of Catalysts 2.5. Temperature-Programmed Reduction (TPR) of Catalysts Temperature-programmed reduction (TPR) tests were performed, in which catalysts C1, C2, and C4 wereTemperature-programmed exposed to a hydrogen reduction flow and (TPR) heated tests to were a temperature performed, of in 900 which◦C catalysts to identify C1, the C2, reduction and peaksC4 (Figure were exposed4). The to TPR a hydrogen profiles flow of the and catalysts heated to derived a temperature from theof 900 sludge °C to ofidentify metallic the reduction mine tailings (C1 andpeaks C2) (Figure were 4). shown The TPR to profiles have three of the reduction catalysts derived stages from since the three sludge defined of metallic peaks mine were tailings observed. (C1 and C2) were shown to have three reduction stages since three defined peaks were observed. However, the presence of a peak at 286 C in C1 was found, which is attributable to the presence of Cu However, the presence of a peak at 286◦ °C in C1 was found, which is attributable to the presence of as observed in other catalysts based on SiO [42]. Cu as observed in other catalysts based on2 SiO2 [42]. The catalystThe catalyst C4 showsC4 shows two two reduction reduction stages: stages: the the first first reduction reduction peak peak appears appears at 465at 465◦C. °C. The The second reductionsecond peak reduction was atpeak 682 was◦C, at both 682 of°C, which both of correspond which correspond to the to reduction the reductio of ironn of iron species, species, and and mainly reductionmainly from reduction Fe3O 4fromto FeO Fe3O [443 to]. FeO [43]. WhenWhen performing performing the the temperature-programmed temperature-programmed re reductionduction assays assays for forcatalyst catalyst C3, C3,the thermal the thermal conductivityconductivity detector detector (TCD) (TCD) signal signal was was very very unstable, unstable, probably probably duedue toto thethe low thermal stability stability of of the catalyst,the catalyst, which waswhich also was observed also observed in catalysts in catalysts derived derived from from the the tannery tannery industry industry [[23].23].

TPR_C1 TPR_C2 TPR_C4 TCD signal, a.u.

100 200 300 400 500 600 700 800 900 Temperature, °C

Figure 4. Temperature-programmed reduction (TPR) profiles for C1, C2, and C4 catalysts in H2 flow. Figure 4. Temperature-programmed reduction (TPR) proﬁles for C1, C2, and C4 catalysts in H2 ﬂow.

2.6. Scanning2.6. Scanning Electron Electron Microscope Microscope with with Energy Energy DispersiveDispersive X-ray X-ray Spectroscopy Spectroscopy (SEM–EDS) (SEM–EDS) MicrographsMicrographs of di offf erentdifferent catalysts catalysts are presentedare presente ind Figure in Figure5 by 5 SEM–EDS, by SEM–EDS, and Figureand Figure6a–d 6a–d presents SEMpresents surface imageSEM surface at a resolution image at a toresolution 5kx. C1 to (Figure 5kx. C15 a)(Figure shows 5a) a shows flat and a flat irregular and irregular crystalline crystalline structure of approximatelystructure of approximately 100 µm in size; 100 itsμm geometry in size; its is geometry better defined is better than defined the other than catalysts.the other catalysts. According to According to the energy-dispersive X-ray spectroscopy (EDS) analysis, iron (22.4 ± 0.1 wt%), gold, the energy-dispersive X-ray spectroscopy (EDS) analysis, iron (22.4 0.1 wt%), gold, and copper were and copper were detected at the surface but less content than the detection± limit. In addition, silica detected at the surface but less content than the detection limit. In addition, silica (13.9 0.2%) and (13.9 ± 0.2%) and aluminum (9.9 ± 0.2%) quantified. The results represent the element percentage± in aluminum (9.9 0.2%) quantified. The results represent the element percentage in the samples. the samples.± C2 (FigureC2 (Figure5b) is5b) a is material a material that that has has a a morphology morphology similar to to C1 C1 but but is issmaller smaller in size, in size, presenting presenting a similara similar surface surface composition, composition, with with iron iron (28.3 (28.30.1 ± wt%),0.1 wt%), gold, gold, and copperand copper being being identified, identified, in addition in ± addition to silica (16.2 ± 0.2%) and aluminum (8.0 ± 0.2%). The presence of sulfur was also identified in some areas, consistent with the results of elemental analysis. C3 (Figure 5c) appears to comprise spheres with well-defined and uniform surfaces with a lamellar and slightly porous texture. The EDS shows the presence of iron (21.9 ± 0.1 wt%) and copper (0.8 ± 0.1 wt%); in some areas, high content of Zn (36.8 ± 1%) was detected. Additionally, chlorine and potassium were observed, probably due to the sludge originating from wastewater treatment. C4 (Figure 5d) presents the most irregular texture, and well-dispersed metallic crystals were observed. In the SEM-EDS, iron content (23.6 ± 0.1 wt%) was identified, and calcium (8.3 ± 2%), sulfur, Sustainability 2020, 12, 9849 7 of 16 to silica (16.2 0.2%) and aluminum (8.0 0.2%). The presence of sulfur was also identified in some ± ± areas, consistent with the results of elemental analysis. C3 (Figure5c) appears to comprise spheres with well-defined and uniform surfaces with a lamellar and slightly porous texture. The EDS shows the presence of iron (21.9 0.1 wt%) and copper ± (0.8 0.1 wt%); in some areas, high content of Zn (36.8 1%) was detected. Additionally, chlorine and ± ± potassium were observed, probably due to the sludge originating from wastewater treatment. C4 (Figure5d) presents the most irregular texture, and well-dispersed metallic crystals were observed.Sustainability In the 2020 SEM-EDS,, 12, x FOR PEER iron REVIEW content (23.6 0.1 wt%) was identified, and calcium7 of (8.3 16 2%), Sustainability 2020, 12, x FOR PEER REVIEW ± 7 of 16 ± sulfur,and carbon carbon were were also also detected detected in addition in addition to stro tontium, strontium, which whichis specific is specific to the activities to the activities of of and carbon were also detected in addition to strontium, which is specific to the activities of hydrocarbonhydrocarbon storage storage from from which which the the samples samples ofof residual sludge sludge were were obtained. obtained. hydrocarbon storage from which the samples of residual sludge were obtained.

(a) (b) (a) (b)

(c) (d) (c) (d) FigureFigure 5. Scanning 5. Scanning electron electron microscope microscope with with energy-dispersive energy-dispersive X-ray X-ray spectroscopy spectroscopy (SEM–EDS) (SEM–EDS) images Figure 5. Scanning electron microscope with energy-dispersive X-ray spectroscopy (SEM–EDS) C1 (a), C2 (b), C3 (c) and C4 (d).images C1 (a), C2 (b), C3 (c) and C4 (d). images C1 (a), C2 (b), C3 (c) and C4 (d).

Figure 6. Scanning electron microscope (SEM) images C1 (a), C2 (b), C3 (c) and C4 (d). Figure 6. Scanning electron microscope (SEM) images C1 (a), C2 (b), C3 (c) and C4 (d). Figure 6. Scanning electron microscope (SEM) images C1 (a), C2 (b), C3 (c) and C4 (d). 2.7. X-ray Diffraction (XRD) 2.7. X-ray Diffraction (XRD) X-ray diffraction results from the calcined catalyst are presented in Figure 7. The peak X-ray diffraction results from the calcined catalyst are presented in Figure 7. The peak identification was be made through platform AtomWork: Inorganic Material Database from NIMS identification was be made through platform AtomWork: Inorganic Material Database from NIMS material database [44]. Diffractograms for C1 (Figure 7a) and C2 (Figure 7b) show that peaks are material database [44]. Diffractograms for C1 (Figure 7a) and C2 (Figure 7b) show that peaks are formed at the same diffraction angle of 2θ. Both catalysts show a higher crystallinity compared to formed at the same diffraction angle of 2θ. Both catalysts show a higher crystallinity compared to catalysts C3 (Figure 7c) and C4 (Figure 7d). The prominent peaks are located around 2θ = 20, 27, 50, catalysts C3 (Figure 7c) and C4 (Figure 7d). The prominent peaks are located around 2θ = 20, 27, 50, Sustainability 2020, 12, 9849 8 of 16

2.7. X-ray Diffraction (XRD) X-ray diffraction results from the calcined catalyst are presented in Figure7. The peak identification was be made through platform AtomWork: Inorganic Material Database from NIMS material database [44]. Diffractograms for C1 (Figure7a) and C2 (Figure7b) show that peaks are formed at the same diffraction angle of 2θ. Both catalysts show a higher crystallinity compared to catalysts C3 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 16 (Figure7c) and C4 (Figure7d). The prominent peaks are located around 2 θ = 20, 27, 50, and 60◦, which correspond to SiO . Additionally, less prominent peaks occur at 2θ = 12, 54, and 65 , which could and 60°, which correspond2 to SiO2. Additionally, less prominent peaks occur at 2θ = 12, 54,◦ and 65°, θ be associatedwhich could with be associated Fe2O3. The with presence Fe2O3. The of other presence peaks of other at 2 peaks= 38, at 44, 2θ and = 38, 82 44,◦ could and 82° be could related be to the interactionrelated to between the interaction Fe and between Al and Fe 2 andθ = Al44, and 65, 2 andθ = 44, 82 65,◦ to and the 82° interaction to the interaction between between Fe Si. Fe InSi. these patterns,In these thin patterns, and well-defined thin and well-defined peaks were peaks also we determined,re also determined, serving serving as evidence as evidence of the of crystalline the naturecrystalline of these nature catalysts of these show catalysts in Figure show6a,b. in Figure 6a,b.

C1 C2 Intensity (a.u) Intensity (a.u)

5 152535455565758595 5 152535455565758595 2ϴ (deg.) 2ϴ (deg.) (a) (b)

C3 C4 Intensity (a.u) Intensity (a.u) Intensity

5 152535455565758595 5 152535455565758595 2ϴ (deg.) 2ϴ (deg.) (c) (d)

FigureFigure 7. 7.X-ray X-ray di diffractionﬀraction (XRD) patterns patterns C1 C1 (a (),a C2), C2 (b), (b C3), C3(c) and (c) and C4 (d C4). (d).

In theIn the diff diffractogramractogram for for C3C3 (Figure 7c),7c), diffraction di ffraction peaks peaks can be can observed be observed at 2θ = 33, at 250,θ =53.9,33, and 50, 53.9, 63.9° corresponding to Fe2O3. Several strong peaks were observed that diffract at 2θ = 30, 32, 35, 36, and 63.9◦ corresponding to Fe2O3. Several strong peaks were observed that diffract at 2θ = 30, 32, 57, and 62.5°, associated with Fe3O4. The peaks at 2θ = 36, 43, and 73° correspond to Zn–Fe 35, 36, 57, and 62.5◦, associated with Fe3O4. The peaks at 2θ = 36, 43, and 73◦ correspond to Zn–Fe interactions. A peak at 2θ = 44.39° can also be observed, which indicates the presence of an alloy of interactions. A peak at 2θ = 44.39 can also be observed, which indicates the presence of an alloy of Cu–Cr [43], which is characteristic◦ of sludge from alloy processes. The diffraction patterns show wide Cu–Crpeaks [43 ],and which a large is characteristic amount of noise of sludge at the from baselin alloye, processes.unlike the Theother di ffcatalysts.raction patternsThis behavior show wide peakscorresponds and a large to amount particles of ofnoise low crystallinity, at the baseline, which unlike is evidenced the other by catalysts.the SEM results, This behavior shown in correspondsFigure to particles6c. of low crystallinity, which is evidenced by the SEM results, shown in Figure6c. SeveralSeveral peaks peaks were were observed observed for for C4 (FigureC4 (Figure7d), 7d), namely namely at 2 atθ =2θ29 =◦ ,29°, which which corresponds corresponds to calciumto sulfate,calcium and atsulfate, 2θ = and31, 35,at 2 38,θ = 41,31, 43,35, 38, 48, 41, 49, 43, and 48, 56 49,◦, relatedand 56°, torelated Fe3O to4. AFe peak3O4. A close peak toclose 2θ to= 532θ◦ =, related53°, to related to iron sulfide, confirms the results of the SEM-EDS test and elemental analysis, indicating the presence of sulfur.

2.8. Characterization Results of Thermoenergetic Properties of Synthesized Materials Sustainability 2020, 12, 9849 9 of 16 iron sulﬁde, conﬁrms the results of the SEM-EDS test and elemental analysis, indicating the presence of sulfur.

2.8. CharacterizationSustainability 2020, 12 Results, x FOR PEER of Thermoenergetic REVIEW Properties of Synthesized Materials 9 of 16 In order to study the thermoenergetic stability of the synthesized materials, the samples of sludge In order to study the thermoenergetic stability of the synthesized materials, the samples of and catalystssludge and were catalysts analyzed were using analyzed diﬀerential using differential scanning calorimetry,scanning calorimetry, in which thein which samples the weresamples exposed to dynamicwere exposed changes to indynamic temperature. changes The in resultingtemperature. heat The ﬂows resulting are expressed heat flows in are the expressed thermograms in the shown in Figurethermograms8a–d. The shown tests in were Figure performed 8a–d. The intests an were inert performed nitrogen atmospherein an inert nitrogen at a temperature atmosphere rangeat a of 25–550temperature◦C. range of 25–550 °C.

0.30 600 0.30 600 ↑endothermic ↑endothermic 0.20 500 0.20 500 -1 0.10 -1 0.10 ˚C 400 400 ˚C 0.00 0.00 300 300 S1 S2 -0.10 -0.10 C1 C2 200 200 DSC, mW*mg DSC, mW*mg DSC, Temperature, Temperature, -0.20 Temperature, -0.20

-0.30 100 -0.30 100

-0.40 0 -0.40 0 0 306090120150180 0 306090120150180 Time, min Time, min (a) (b)

0.15 600 0.30 600 ↑endothermic ↑endothermic 0.10 500 0.20 500 0.05

-1 -1 0.10

0.00 ˚C ˚C 400 400 -0.05 0.00 -0.10 300 300 S3 S4 -0.10 -0.15 C3 C4 200 200

DSC, mW*mg -0.20 DSC, mW*mg Temperature, Temperature, -0.20 Temperature, -0.25 100 -0.30 100 -0.30 -0.35 0 -0.40 0 0 306090120150180 0 306090120150180 Time, min Time, min (c) (d)

FigureFigure 8. Thermograms 8. Thermograms from from didifferentialﬀerential sca scanningnning calorimetry calorimetry (DSC) (DSC) for C1 for (a), C1 C2 ( a(b),), C2 C3 ((bc)), and C3 C4 (c) and C4 (d(d).).

The samples show a peak of endothermic energy in the interval of 25–130 °C (the first 20 min of The samples show a peak of endothermic energy in the interval of 25–130 ◦C (the ﬁrst 20 min of the the test). This corresponds to the decomposition of humidity and low-boiling-point volatiles present test). This corresponds to the decomposition of humidity and low-boiling-point volatiles present in all in all types of sewage sludge [45], which can be adsorbed on the surface and in the pores of the typesmaterials. of sewage sludge [45], which can be adsorbed on the surface and in the pores of the materials. S1 (FigureS1 (Figure8a) and8a) and S2 (FigureS2 (Figure8b) 8b) have have curves curves with with similarsimilar behaviors behaviors because because the the two two sludges sludges comecome from from metal metal mine mine tailings, tailings, as as do do the the catalysts catalysts synthesized from from both both of these of these sludges. sludges. The initial The initial stabilitystability of the of the sludges sludges may may also also be be the the reason reason that their their corresponding corresponding synthesized synthesized catalysts catalysts are also are also stable.stable. C2 hadC2 had slightly slightly improved improved energy energy capacitycapacity th thanan S2, S2, probably probably because because of the of thehigher higher amount amount of of oxidesoxides in S2 in because S2 because it is it anis an aged aged sludge. sludge. C3 (FigureC3 (Figure8c), 8c), compared compared to to S3, S3, had had improved improved thermal stability stability because because it did it did not notpresent present the the endothermic peak that is observed in S3 at 200 °C, which is related to the decomposition of acids used endothermic peak that is observed in S3 at 200 ◦C, which is related to the decomposition of acids used in the steelwork process that accumulate in the sludge from water treatment. The peak at 500 °C present in C3 indicates the change in the iron oxides present from γ-Fe2O3 to α-Fe2O3 [46]. Sustainability 2020, 12, 9849 10 of 16

in the steelwork process that accumulate in the sludge from water treatment. The peak at 500 ◦C present in C3 indicates the change in the iron oxides present from γ-Fe2O3 to α-Fe2O3 [46]. C4 (Figure8d) noticeably improved its thermal stability compared to S4 due to eliminating the endothermic peaks at 220 and 420 ◦C that is assigned to the decomposition of high-boiling-point and high-melting-point hydrocarbons of asphaltenes that are adsorbed in the bottom sludge of hydrocarbon storage tanks [37]. Differential scanning calorimetry shows the value of the heat flux rate (W) as a function of dynamic temperature changes, which can be used to obtain the specific heat considering Equation (1),

Φm Φ Cp = − 0 (1) m β × where

Cp: specific heat, kJ/kg K · Φm sample heat flow rate, W Φ0: zero-line heat flow rate, W m: mass of sample, g β: heating rate, K/s

The average Cp values in the temperature range of 130–550 ◦C are shown in Table6. The Cp values are important to extrapolate the use of these catalysts in industrial applications, mainly to energy and exergy balance.

Table 6. Average Cp (kJ/kg K) of sludges and catalysts.

Sludge Cp, kJ/kg K Catalysts Cp, kJ/kg K S1 0.992 C1 1.155 S2 1.018 C2 1.192 S3 0.620 C3 0.700 S4 1.434 C4 0.951

There are no significant differences between the average Cp values of the synthesized catalysts, which is consistent because all catalysts have iron in their composition. The average specific heat increases in the catalysts at C1, C2, and C3 compared to the sludge from which they are derived. In C4, an inverse behavior is observed, which is probably due to S4 was impregnated with hydrocarbon residues with a higher specific capacity. C1 and C2 have a higher specific capacity compared to sludge from other industries because these materials have traces of gold, which provides a higher energy capacity. The values of Cp were similar to those of other catalysts; it can be seen that the specific capacities of C1 and C2 are similar to the mineral modernite (1.22 kJ/kg K) [47]. In comparison, C3 and C4 have · similar specific capacities to the catalysts Fe O /Cr O (0.85 kJ/kg K) [48] and Fe O (0.88 kJ/kg K) [49]. 3 4 2 3 · 2 3 · 2.9. Catalytic Activity Evaluation In order to establish the catalytic activity of the synthesized catalysts, two types of tests were performed: (i) the chemisorption of CO through pulses and (ii) the catalytic cracking reaction of crude oil. The CO chemisorption phase (Figure9a) was carried out at 50 ◦C considering the predominant iron content of the samples. It is showed that the amount of carbon monoxide adsorbed at the operating conditions was minimal for C2 and C3, and slight adsorption was observed after 19 min reaction time. For C4, a rapid saturation of the active centers was shown, demonstrated by the constant trend from 16 min onwards. C1 showed more significant adsorption compared to the other catalysts, and even had Sustainability 2020, 12, 9849 11 of 16 an increasing trend over time, which could be attributable to the more considerable amount of copper in this catalyst compared with the others as well as to the capacity for more significant reduction that could be seen in TPR. At the end of the test, it was observed that the catalyst had a black color due to Sustainability 2020, 12, x FOR PEER REVIEW 11 of 16 the carbon deposition of CO on the surface. FollowingFollowing thethe adsorptionadsorption of of CO, CO, the the samples samples were were exposed exposed to controlled to controlled combustion combustion in a inthermobalance a thermobalance to toverify verify the the effect eff ectof chemisorption. of chemisorption. The Theresults results of this of test this are test shown are shown in Figure in Figure 9b, in9 b, in which it it can can be be observed observed that that C1 C1 had had a higher a higher amount amount of carbon of carbon on its on desorbed its desorbed surfaces. surfaces.

1.0 C1 0.9 C2 0.8 C3 C4 0.7 0.6 0.5 0.4 0.3

Relative loss ofloss mass Relative 0.2 0.1 zone I zone II zone III 0.0 20 80 140 200 260 320 380 440 500 560 620 Temperature, °C (a) (b)

Figure 9. CO chemisorption (a) and C-burned compounds from the catalytic surface (b). Figure 9. CO chemisorption (a) and C-burned compounds from the catalytic surface (b).

EvaluationEvaluation of of the the catalytic catalytic propertiesproperties of the four catalysts catalysts was was carried carried out out using using an an oil oil cracking cracking reaction.reaction. One One reaction reaction without without a a catalystcatalyst and one wi withth a a commercial commercial catalyst catalyst were were performed performed in order in order to contrast the catalytic activity. The reactions were carried out by thermogravimetry at 400 °C, in to contrast the catalytic activity. The reactions were carried out by thermogravimetry at 400 ◦C, which the loss of mass was produced by the action of the catalysts. The results are reported in Table in which the loss of mass was produced by the action of the catalysts. The results are reported in 7, where it is evident that the catalysts promoted mass loss in the crude oil catalytic cracking reaction Table7, where it is evident that the catalysts promoted mass loss in the crude oil catalytic cracking since the mass losses were higher than the reaction without the catalyst. It is evident that the C1 reaction since the mass losses were higher than the reaction without the catalyst. It is evident that the catalyst generates the most mass loss due to catalytic activity, while C4 produces the least mass loss; C1the catalyst values generates obtained theare mostsimilar mass to thos losse duegenerated to catalytic by the activity, commercial while catalyst. C4 produces the least mass loss; the valuesBased obtained on the aremass-loss similar todata, those a kinetic generated model by thewas commercial done applying catalyst. the integral method of linearizationBased on the of mass-losskinetic expression data, a kinetic 2 m0.5 modelvs. time. was The done kinetic applying order the of 0.5 integral provided method the ofbest linearization fit with 0.5 ofthe kinetic experimental expression data 2 m accordingvs. time. to The the kineticdetermination order of coefficient 0.5 provided (R2 the). The best experimental ﬁt with the experimentaldata were 2 datareplaced according in the to kinetic the determination expression for coe eachﬃcient time (R to). calculate The experimental the average data rate wereof mass replaced loss, considering in the kinetic expressionthe Fe mass for content. each time It was to calculate observed the that average C4 and rateC1 have of mass a greater loss, consideringrate of the reaction; the Fe massC3 has content. the It wasworst observed rate compared that C4 with and the C1 other have synthesized a greater rate catalysts. of the reaction; C3 has the worst rate compared with the other synthesized catalysts. Table 7. Loss of mass generated by a catalytic cracking reaction with thermogravimetry (heating ramp Table10 °C/min 7. Loss and of masstime on generated stream = by 1 h). a catalytic cracking reaction with thermogravimetry (heating ramp 10 ◦C/min and time on streamLoss of= 1 h). Rate of Reaction (avg), Samples Kinetic Expression R2 Mass, % mgoil. (min.mgFe)−1 2 Rate of Reaction (avg), mgoil. Samples Loss of Mass, % Kinetic Expression R 1 Reaction without . (min.mgFe)− 55.31 = 0.0015 0.921 -- Reactioncatalyst without catalyst 55.31 dm = 0.0015 m0.5 0.921 – dt A Reaction + C1 70.38 dm = 0.0018. m0.5 0.948 0.18 Reaction + C1 70.38 = 0.0018dt A 0.948 0.18 Reaction + C2 69.13 dm = 0.0016 m0.5 0.933 0.14 dt A ReactionReaction + C2+ C3 69.13 69.25 = 0.0016dm = 0.0015 . m0.5 0.9330.922 0.14 0.07 dt A Reaction + C4 66.89 dm = 0.0017 m0.5 0.919 0.20 dt A ReactionReaction with commercial + C3 catalyst 69.25 62.29 = 0.0015dm = 0.0019 . m0.5 0.9220.950 0.07 – dt A Reaction + C4 66.89 = 0.0017 . 0.919 0.20 Reaction with 62.29 = 0.0019 . 0.950 -- commercial catalyst

Sustainability 2020, 12, 9849 12 of 16

2.10. Synthesized Material Sustainability In order to quantify the sustainability of the process, the loss of mass in the diﬀerent steps of the synthesis—drying and calcination—were calculated and are shown in Table8. The Environmental Factor (E-Factor = mass of waste/mass of the desired product) [50] is included.

Table 8. Yield of the catalyst synthesized process.

Initial Sludge Moisture and Organic Loss, Yield of Catalytic Samples E-Factor Mass, g Volatile Loss, wt% wt% Material, wt% 1 100.64 0.00 0.32 99.68 0.003 2 99.77 10.32 0.68 88.99 0.124 3 100.49 0.39 2.14 97.47 0.026 4 100.37 8.94 16.06 75.00 0.333

The results showed that the synthesis of C1 and C3 has a greater yield of the product than the other catalyst, while C4 has the lesser yield; due to the nature of S4, this sludge was the most diﬃcult to handle in the lab. The ideal E-Factor tends to be zero; in this work, they are less than 0.333; the values ratify the sustainability of the waste valorization to transform sludge in the catalyst.

3. Materials and Methods

3.1. Catalyst Characterization The characterization of catalysts by surface area and pore volume of catalytic materials, specific surface (BET method), temperature-programmed reduction (TPR), X-ray diffraction (XRD), and scanning electron microscopy (SEM/EDS) was carried out using the equipment and methods described in previous works [21,22]. The physical properties of catalysts, such as grain size and diameter distribution, were determined using a granulometric analysis system with equipment that performs digital image processing (Retsch-Camsizer, Haan, Germany) following the standard ISO 13322-2. For this granulometric analysis, 10 g of sample was used, and the grain size was studied in the range of 80–560 µm. The metal content was determined in 200 mg of a sample by atomic absorption spectrophotometry (AAS) using an atomic absorption spectrophotometer (PerkinElmer-AAnalyst 400, Waltham, MA, USA) with an atmosphere of ethylene (grade 2.7, INDURA, Quito, Pichincha, Ecuador). For these tests, the calibration of equipment was performed using WinLab32 AATM 6.0 software with Cu, Fe, Cr, Au, and Cd standards (0.5 M/HNO3, Teknokroma, Barcelona, Spain). Before metal determination, the samples were prepared by digestion in a microwave system (Milestone-ETHOS UP, Sorisole, BG, Italy). The specific heat capacity (Cp) of sludge and catalysts was evaluated through differential scanning calorimetry (DSC) (NETZSCH-DSC 3500 Sirius, Berlin, Germany) using 10–35 mg samples. The heat flux for all materials was determined in the temperature range of 25–550 ◦C at a heating rate of 5 C min 1 under an inert N atmosphere (33 mL min 1, 99.999%, INDURA, Quito, Pichincha, ◦ · − 2 · − Ecuador). Aluminum crucibles (40 µL) with perforated lids were used in these tests. Before evaluating the catalytic activity, catalysts were reduced in an atmosphere of 10% H2/Ar 1 (50 mL min− , 99.999%, INDURA, Quito, Pichincha, Ecuador) with a temperature ramp of 10 ◦C/min from room temperature to 900 ◦C using chemisorption equipment (Micromeritics-AutoChem II, 2920, Norcross, GA, USA). To guarantee representative results, all the characterization analyses in both sludge and catalysts were carried out in triplicate. Sustainability 2020, 12, 9849 13 of 16

3.2. Catalytic Material Evaluation (Catalytic Tests) The ability of catalysts to adsorb CO was studied by chemisorption using CO pulses (Micromeritics-AutoChem II, 2920, Norcross, GA, USA) in 220 mg catalyst samples. Firstly, catalysts were reduced as previously described, after which purging with He (50 mL min 1, 99.999%, INDURA, · − Quito, Pichincha, Ecuador) was carried out until a temperature of 50 ◦C was reached. Then, chemisorption was done at this temperature, exposing catalysts to a ﬂow of 10% CO/He (50 mL min 1, · − 99.999%, INDURA, Quito, Pichincha, Ecuador). Between 6 and 10 pulses of CO were performed, depending on the adsorption capacity of the samples, which was detected by a TCD sensor. Temperature-programmed oxidation (TPO) tests were conducted using a thermobalance (Mettler Toledo-TGA1 SF/1100, Columbus, OH, USA) to verify the carbon deposition on the catalyst surface after CO chemisorption. Catalyst samples of 70 mg were exposed to an atmosphere of synthetic air (50 mL min 1, 99.999%, INDURA, Quito, Pichincha, Ecuador) to burn the carbonaceous material. · − This test was performed with a heating rate of 15 C min 1 from 40 to 600 C. ◦ · − ◦ The catalytic activity of the synthesized materials was evaluated with a thermocatalytic decomposition reaction of crude oil samples (28.4◦ API, PETROECUADOR, Esmeraldas, Esmeraldas, Ecuador). The thermogravimetric analysis was realized following ASTM E 914, DIN 51006, ISO 711 in a thermobalance (Mettler Toledo-TGA1 SF/1100, Columbus, OH, USA). With this test, it was possible to determine the loss of mass generated by the catalysts synthesized from sludge. Before this study, the catalytic material was reduced in a 10% H2/Ar ﬂow. The decomposition reaction was performed in an N atmosphere (50 mL min 1, 99.999%, INDURA, 2 · − Quito, Pichincha, Ecuador) with a space-time of 0.15–0.17 g catalyst g 1 crude oil, using dynamic · − ramping at a heating rate of 10 C min 1 from room temperature to 400 C. The reaction took place for ◦ · − ◦ one hour time on stream, with the loss in weight recorded every 35 s. Assays without catalysts and using a commercial FCC (Fluid cracking catalytic) catalyst (GRACE-ResidCrackeR, Columbia, MD, USA) were carried out to allow a comparison of the catalytic performance on crude oil decomposition.

4. Conclusions

The best conditions for synthesis were the drying of sludge between 110 and 130 ◦C and calcination for C1 and C2 at 300 ◦C, C3 at 500 ◦C, and C4 at 450 ◦C (the higher temperature is due to the higher organic content of S4). The synthesized catalysts showed relatively low specific surface areas (C1: 4253; C2: 3034; C3: 7971; and C4: 16,783 m2 g 1) compared to other catalysts synthesized from sludge. · − The catalysts were mainly made of iron—between 2.5 and 7.5% wt—and the presence of other metals also corresponded with the production processes from which the waste sludge originated, such as traces of gold and copper in the catalysts derived from mining tailings. The process of catalyst synthesis from sludge resulted in the improved thermal-energetic stability of these waste materials. C1 and C2 catalysts from the mining industry showed a higher specific capacity and thermal stability than other catalysts and would be the best option for use in high thermoenergy processes. Evidence was provided for the catalytic activity of the synthesized materials in reactions with CO, mainly for C1. In hydrocarbon cracking tests, it was observed that mine tailings catalysts generated the most significant weight loss, with values comparable to those of commercial catalysts. The rate of reaction based on the iron content reveals that C1 and C4 have similar values, and it can be used as a catalyst in several industrial chemical reactions. The synthesis of catalysts from waste sludge would be an alternative for the recovery of wastes from the mining and steel industries and the storage of hydrocarbons, with a low E-factor that ratifies the sustainability of the process. This process promotes a circular economy in Ecuador, based on the recovery of industrial wastes that are cataloged as hazardous. Sustainability 2020, 12, 9849 14 of 16

Author Contributions: Conceptualization, C.M.-C., A.D.L.R., J.A., and D.E.M.E.; methodology, C.M.-C., J.A., and D.E.M.E.; validation, C.M.-C., A.D.L.R., and D.E.M.E.; formal analysis, D.E.M.E.; investigation, G.C.-L., E.B.-Q., B.G.L., and J.A.; resources, A.D.L.R. and C.M.-C.; data curation, J.A. and A.D.L.R.; writing—original draft preparation, G.C.-L., E.B.-Q., B.G.L., and A.D.L.R.; writing—review and editing, C.M.-C. and D.E.M.E.; supervision, C.M.-C. and A.D.L.R.; project administration, C.M.-C.; funding acquisition, C.M.-C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Secretaría Nacional de Educación Superior del Ecuador—SENESCYT and Universidad Central del Ecuador—Research Program Inédita 2018, grant number 20180150 CI. Acknowledgments: The authors are grateful to the Chemical Engineering Faculty (UCE) for the support and the use of laboratory facilities; to P. Londoño and E. Villamarin from the Laboratory of Catalyst (FIQ-UCE) for their technical assistance; to H. Solís, G. Gordillo, A. Reyes, A. Simbaña, and E. Morales (FIQ UCE) for their research assistance; to Curtiembre La Alborada and other Ecuadorian companies that provide the samples of sludges. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. IEA World Energy Outlook—Topics. Available online: https://www.iea.org/topics/world-energy-outlook (accessed on 2 August 2020). 2. Ministerio del Ambiente. Ecuador Acuerdo Ministerial 142 Sustancias Peligrosas; Ministerio del Ambiente: Quito, Ecuador, 2012. (In Spanish) 3. Instituto Nacional de Estadística y Censos-Ecuador (INEC). Módulo Ambiental de la Encuesta Estructural Empresarial (ENESEM), año 2017; Ecuador en cifras: Quito, Ecuador, 2019. (In Spanish) 4. UNACEM. Resumen del Informe Final de la Consultoria Para la Fase 1 del Libro Blanco de Economía Circular; UNACEM: Guayaquil, Ecuador, 2020. (In Spanish) 5. United Nations Educational, Scientific and Cultural Organization. Informe Mundial de Naciones Unidas Sobre el Desarrollo de Los Recursos Hídricos 2019: No Dejar a Nadie Atrás; UN: New York, NY, USA, 2019; ISBN 978-92-1-004594-0. (In Spanish) 6. Suárez-Macías, J.; Terrones-Saeta, J.M.; Iglesias-Godino, F.J.; Corpas-Iglesias, F.A. Retention of Contaminants Elements from Tailings from Lead Mine Washing Plants in Ceramics for Bricks. Minerals 2020, 10, 576. [CrossRef] 7. Ince, C. Reusing gold-mine tailings in cement mortars: Mechanical properties and socio-economic developments for the Lefke-Xeros area of Cyprus. J. Clean. Prod. 2019, 238, 117871. [CrossRef] 8. Yildirim Ozen, M.; Moroydor Derun, E. A comparative study: Effects of different nanoparticles on the properties of gold mine tailings containing cement mortars. Constr. Build. Mater. 2019, 202, 396–405. [CrossRef] 9. Kim, Y.; Lee, Y.; Kim, M.; Park, H. Preparation of high porosity bricks by utilizing red mud and mine tailing. J. Clean. Prod. 2019, 207, 490–497. [CrossRef] 10. Wołowiec, M.; Komorowska-Kaufman, M.; Pruss, A.; Rzepa, G.; Bajda, T. Removal of Heavy Metals and Metalloids from Water Using Drinking Water Treatment Residuals as Adsorbents: A Review. Minerals 2019, 9, 487. [CrossRef] 11. Liu, Y.; Khan, A.; Wang, Z.; Chen, Y.; Zhu, S.; Sun, T.; Liang, D.; Yu, H. Upcycling of Electroplating Sludge to Prepare Erdite-Bearing Nanorods for the Adsorption of Heavy Metals from Electroplating Wastewater Effluent. Water 2020, 12, 1027. [CrossRef] 12. Ouyang, D.; Zhuo, Y.; Hu, L.; Zeng, Q.; Hu, Y.; He, Z. Research on the Adsorption Behavior of Heavy Metal Ions by Porous Material Prepared with Silicate Tailings. Minerals 2019, 9, 291. [CrossRef] 13. Mombelli, D.; Barella, S.; Gruttadauria, A.; Mapelli, C. Iron Recovery from Bauxite Tailings Red Mud by Thermal Reduction with Blast Furnace Sludge. Appl. Sci. 2019, 9, 4902. [CrossRef] 14. Ubaldini, S.; Guglietta, D.; Vegliò, F.; Giuliano, V. Valorization of Mining Waste by Application of Innovative Thiosulphate Leaching for Gold Recovery. Metals 2019, 9, 274. [CrossRef] 15. Carneiro, J.; Tobaldi, D.M.; Capela, M.N.; Seabra, M.P.; Labrincha, J.A. Waste-Based Pigments for Application in Ceramic Glazes and Stoneware Bodies. Materials 2019, 12, 3396. [CrossRef][PubMed] 16. Chakravarthy, C.; Chalouati, S.; Chai, Y.E.; Fantucci, H.; Santos, R.M. Valorization of Kimberlite Tailings by Carbon Capture and Utilization (CCU) Method. Minerals 2020, 10, 611. [CrossRef] Sustainability 2020, 12, 9849 15 of 16

17. Klose, F.; Scholz, P.; Kreisel, G.; Ondruschka, B.; Kneise, R.; Knopf, U. Catalysts from waste materials. Appl. Catal. B Environ. 2000, 28, 209–221. [CrossRef] 18. Herrera, S.L.; Hoyos, D.Á.; Palacio, L.A.; Pizarro, J.L.; Aguado, R. Synthesis of Industrial Waste Based Metal Catalysts for Oxidative Dehydrogenation of Propane. Ind. Eng. Chem. Res. 2013, 52, 7341–7349. [CrossRef] 19. Sanchis, R.; Dejoz, A.; Vázquez, I.; Vilarrasa-García, E.; Jiménez-Jiménez, J.; Rodríguez-Castellón, E.; López Nieto, J.M.; Solsona, B. Ferric sludge derived from the process of water purification as an efficient catalyst and/or support for the removal of volatile organic compounds. Chemosphere 2019, 219, 286–295. [CrossRef] 20. Yi, X.; Yu, Y.; Huang, F.; Ding, T.; Zhang, Z.; Feng, J.; Baell, J.B.; Huang, H. Turning Waste into Valuable Catalysts: Application of Surface-Modified Sewage Sludge in N–H Insertion Reaction. Ind. Eng. Chem. Res. 2020, 59, 4854–4863. [CrossRef] 21. Montero, C.; Castañeda, K.; Oña, M.; Flores, D.R.; De La Rosa, A. Catalyst Based on Sludge Derived from Wastewater Treatment of Textile Industry. Chem. Eng. Trans. 2018, 70, 931–936. [CrossRef] 22. Villamarin-Barriga, E.; Canacuán, J.; Londoño-Larrea, P.; Solís, H.; De La Rosa, A.; Saldarriaga, J.F.; Montero, C. Catalytic Cracking of Heavy Crude Oil over Iron-Based Catalyst Obtained from Galvanic Industry Wastes. Catalysts 2020, 10, 736. [CrossRef] 23. Colta Ponce, D.X.; Quishpe Bahamontes, L.V. Síntesis de un Material Catalítico a Partir de Lodos Residuales de la Industria de la Curtiembre. Bachelor’s Thesis, Facultad de Ingeniería Química, Universidad Central del Ecuador, Quito, Ecuador, 2017. (In Spanish) 24. Moreno Moyano, S.K. Descomposición Catalítica de CH4 Utilizando Catalizadores Derivados de lodos Residuales de las Industrias: Textil, Galvanoplastia y Curtiembre. Bachelor’s Thesis, Facultad de Ingeniería Química, Universidad Central del Ecuador, Quito, Ecuador, 2020. (In Spanish) 25. Xu, B.; Ding, T.; Zhang, Y.; Wen, Y.; Yang, Z.; Zhang, M. A new efficient visible-light-driven composite photocatalyst comprising ZnFe2O4 nanoparticles and conjugated polymer from the dehydrochlorination of polyvinyl chloride. Mater. Lett. 2017, 187, 123–125. [CrossRef] 26. Gonzalez-Casamachin, D.A.; Rivera De la Rosa, J.; Lucio–Ortiz, C.J.; Sandoval-Rangel, L.; García, C.D.

Partial oxidation of 5-hydroxymethylfurfural to 2,5-furandicarboxylic acid using O2 and a photocatalyst of a composite of ZnO/PPy under visible-light: Electrochemical characterization and kinetic analysis. Chem. Eng. J. 2020, 393, 124699. [CrossRef]

27. Halasi, G.; Gazsi, A.; Bánsági, T.; Solymosi, F. Catalytic and photocatalytic reactions of H2+CO2 on supported Au catalysts. Appl. Catal. A Gen. 2015, 506, 85–90. [CrossRef] 28. Zuo, C.; Tian, Y.; Zheng, Y.; Wang, L.; Fu, Z.; Jiao, T.; Wang, M.; Huang, H.; Li, Y. One step oxidative

esterification of methacrolein with methanol over Au-CeO2/γ-Al2O3 catalysts. Catal. Commun. 2019, 124, 51–55. [CrossRef] 29. Liao, X.; Chu, W.; Dai, X.; Pitchon, V. Bimetallic Au–Cu supported on ceria for PROX reaction: Effects of Cu/Au atomic ratios and thermal pretreatments. Appl. Catal. B Environ. 2013, 142–143, 25–37. [CrossRef] 30. Torres-Luna, J.A.; Carriazo, J.G.; Sanabria-González, N.R. Calcination Temperature Effect on structural and

textural properties of Fe(iii)-TiO2. Rev. Fac. Cienc. Básicas 2014, 10, 186–195. [CrossRef] 31. Guerrero Fajardo, C.A.; Sánchez Castellanos, F.J.; Roger, A.-C.; Courson, C. Síntesis sol-gel de catalizadores de hierro soportados sobre sílice y titania para la oxidación selectiva de metano hasta formaldehído. Ing. Investig. 2008, 28, 72–80. (In Spanish)

32. Junior, S.A.F.; de Sousa, J.F.; Benachour, M.; Rojas, L.O.A. Wet Oxidation of Phenols using Fe-CeO2, K-MnO2/CeO2/Paligorskite and Fe/Palygorskite Catalysts. Inf. Technol. 2011, 22, 55–68. [CrossRef] 33. Guerrero Fajardo, C.A.; Sánchez Castellanos, F.J. Síntesis de catalizadores de Fe-Mo soportados sobre sílice para la oxidación selectiva de metano hasta formaldehído. Ing. Investig. 2009, 29, 53–59. (In Spanish) 34. Santos, J.L.; Reina, T.R.; Ivanov, I.; Penkova, A.; Ivanova, S.; Tabakova, T.; Centeno, M.A.; Idakiev, V.;

Odriozola, J.A. Multicomponent Au/Cu-ZnO-Al2O3 catalysts: Robust materials for clean hydrogen production. Appl. Catal. A Gen. 2018, 558, 91–98. [CrossRef] 35. Mierczynski, P.; Vasilev, K.; Mierczynska, A.; Maniukiewicz, W.; Szynkowska, M.I.; Maniecki, T.P. Bimetallic Au–Cu, Au–Ni catalysts supported on MWCNTs for oxy-steam reforming of methanol. Appl. Catal. B Environ. 2016, 185, 281–294. [CrossRef] 36. Sun, Z.-X.; Zheng, T.-T.; Bo, Q.-B.; Du, M.; Forsling, W. Eﬀects of calcination temperature on the pore size and wall crystalline structure of mesoporous alumina. J. Colloid Interface Sci. 2008, 319, 247–251. [CrossRef] Sustainability 2020, 12, 9849 16 of 16

37. Zubaidy, E.A.H.; Abouelnasr, D.M. Fuel recovery from waste oily sludge using solvent extraction. Process Saf. Environ. Prot. 2010, 88, 318–326. [CrossRef] 38. Rostrup-Nielsen, J.R. Sulfur Poisoning. In Progress in Catalyst Deactivation; Figueiredo, J.L., Ed.; Springer: Dordrecht, The Netherlands, 1982; pp. 209–227. 39. Zhu, F.; Jiang, H.; Zhang, Z.; Zhao, L.; Wang, J.; Hu, J.; Zhang, H. Research on Drying Effect of Different Additives on Sewage Sludge. Procedia Environ. Sci. 2012, 16, 357–362. [CrossRef] 40. Xia, Y.; Zhan, W.; Guo, Y.; Guo, Y.; Lu, G. Fe-Beta zeolite for selective catalytic reduction of NOx with NH3: Influence of Fe content. Chin. J. Catal. 2016, 37, 2069–2078. [CrossRef] 41. Issangya, A.; Hays, R.; Cocco, R.; Knowlton, T.; Karry, R.S. An Acoustic Method for the Measurement of Minimum Fluidization and Bubbling Properties of Group a Solids; AIChE: San Francisco, CA, USA, 2013.

42. Chun, D.H.; Park, J.C.; Lee, H.-T.; Yang, J.-I.; Hong, S.; Jung, H. Eﬀects of SiO2 Incorporation Sequence on the Catalytic Properties of Iron-Based Fischer–Tropsch Catalysts Containing Residual Sodium. Catal. Lett. 2013, 143, 1035–1042. [CrossRef] 43. Fakeeha, A.; Khan, W.; Ibrahim, A.; Al-Otaibi, R.; Alfatesh, A.; Soliman, M.; Abasaeed, A. Alumina supported iron catalyst for hydrogen production: Calcination study. Int. J. Adv. Chem. Eng. Biol. Sci. 2015, 2, 139–141. [CrossRef] 44. National Institute for Materials Science NIMS Materials Database (MatNavi). Available online: https: //mits.nims.go.jp/index_en.html (accessed on 12 May 2020). 45. Rojas-Aguilar, A.; Ginez-Carbajal, F.; Orozco-Guare´no,E.; Flores-Segura, H. Measurement of enthalpies of vaporization of volatile heterocyclic compounds by DSC. J. Therm. Anal. Calorim. 2005, 79, 95–100. [CrossRef] 46. Pérez, M.C.R.; Herrera, J.A.P.; Priego, F.J.A. Estudio De La Descomposición Térmica De Los Escombros Lateríticos De Moa. Rev. Cuba. Quím. 2005, 17, 104–110. (In Spanish) 47. Findikakis, A. Heat Capacity Analysis Report; Yucca Mountain Project: Las Vegas, NV, USA, 2004. 48. Twigg, M.V. Catalyst Handbook; Routledge: Abingdon, UK, 2018; ISBN 978-1-315-13886-2. 49. Lemire, R.; Palmer, D.; Taylor, P.; Schlenz, H. Chemical Thermodynamics—Chemical Thermodynamics of Iron Part 2; OECD: Paris, France, 2020; Volume 13b. 50. Dicks, A.P.; Hent, A. The E Factor and Process Mass Intensity. In Green Chemistry Metrics: A Guide to Determining and Evaluating Process Greenness; Dicks, A.P., Hent, A., Eds.; SpringerBriefs in Molecular Science; Springer International Publishing: Cham, Switzerland, 2015; pp. 45–67, ISBN 978-3-319-10500-0.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional aﬃliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).